Benchmarking Regional Health Management II (BenRHMII) ___________________________________________________________________________________________________________________________________________________________________ Table 5: Set of variables for the clustering No. Variable Acronym 1 Population density POP_DENS 2 Proportion of population aged 65 and older in relation to the total population YEAR_65 3 Sex ratio SEX_RAT 4 Unemployment rate UNEMPL 5 Disposable income of household per inhabitant INCOM 6 Number of physicians per 100.000 inhabitants PHYSIC 7 Type of health care system HEALTH 8 Types of system of government GOVERN Once the correlation analysis had been concluded, a standardisation procedure to re-scale the remaining numeric variables was carried out. The variable mean was subtracted from each variable and the result divided by the standard deviation. After the standardisation of variables, the categorical variables were encoded. The categories of the non-numeric variables were given numeric values. Table 6: Data framework Sources: • EUROSTAT. Regional Statistics (2006): http://epp.eurostat.cec.eu.int/portal, 16.06.06. • Bravo, Y., Ferguson, B., Iglesias, C. (2005). The Use of Cluster Analysis to identify Factors that Influence the Establishment of Health Technologies Assessment (HTA) Agencies. Presentation. Centre for Health Economics. University of York. Humber & Yorkshire Observatory of Public Health. • Saltman, R.B., Busse, R., Figueras, J. (2002). Social Health Insurance Systems in Western Europe. European Observatory on Health Systems and Policies Series. Open University Press. • European Observatory on Health Care Systems. (2000). Health Care Systems in Transition: Lithuania. European Observatory on Health Care Systems. - 57 -